{
 "metadata": {
  "name": "",
  "signature": "sha256:fa80e244f8b4f4d284011064a66cc799c3e264607b7bd2632f6d4c5c13e3b195"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# LegCoHK\n",
      "\n",
      "Author: [Pili Hu](http://hupili.net/)\n",
      "\n",
      "GitHub repo: https://github.com/hupili/legcohk\n",
      "\n",
      "Related notebooks:\n",
      "\n",
      "   * PCA for [ENGG4030](https://course.ie.cuhk.edu.hk/~engg4030/): http://bit.ly/1riabfV\n",
      "   * Recommender System for [ENGG4030](https://course.ie.cuhk.edu.hk/~engg4030/): http://bit.ly/QwNvLZ\n",
      "   * Graph Analysis for [ENGG4030](https://course.ie.cuhk.edu.hk/~engg4030/): http://bit.ly/1mxjuqu\n",
      "   \n",
      "Compared with the above notebooks, \n",
      "this repo contains more compact notes covering the whole data mining flow --\n",
      "from data collection to final visualization.\n",
      "Interpretations will note be provided in the notes directly.\n",
      "If you have interest, you can dump your thoughts on the [issue tracker](https://github.com/hupili/legcohk/issues).\n",
      "You can also request other features there."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Preparation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "import requests\n",
      "import pylab as pl\n",
      "from pyquery import PyQuery as pq\n",
      "import numpy as np\n",
      "import matplotlib as plt\n",
      "import scipy\n",
      "import pandas as pd\n",
      "from lxml import etree"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "seed_pages = [\n",
      "    'http://www.legco.gov.hk/general/english/counmtg/yr12-16/mtg_1213.htm',\n",
      "    'http://www.legco.gov.hk/general/english/counmtg/yr12-16/mtg_1314.htm'\n",
      "]\n",
      "def crawl_seed(seed):\n",
      "    d = pq(seed)\n",
      "    return d('a').map(lambda i, a: a.attrib.get('name', None)).filter(lambda i, s: s.startswith('cm20'))\n",
      "meetings = reduce(list.__add__, map(crawl_seed, seed_pages), [])\n",
      "print meetings"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['cm20121010', 'cm20121017', 'cm20121024', 'cm20121031', 'cm20121101', 'cm20121107', 'cm20121114', 'cm20121121', 'cm20121128', 'cm20121205', 'cm20121210', 'cm20121212', 'cm20121219', 'cm20130109', 'cm20130116', 'cm20130117', 'cm20130123', 'cm20130130', 'cm20130206', 'cm20130220', 'cm20130227', 'cm20130320', 'cm20130327', 'cm20130417', 'cm20130424', 'cm20130508', 'cm20130509', 'cm20130515', 'cm20130522', 'cm20130529', 'cm20130605', 'cm20130619', 'cm20130626', 'cm20130703', 'cm20130710', 'cm20130711', 'cm20130717', 'cm20131009', 'cm20131016', 'cm20131017', 'cm20131023', 'cm20131030', 'cm20131106', 'cm20131113', 'cm20131120', 'cm20131127', 'cm20131204', 'cm20131211', 'cm20131218', 'cm20140108', 'cm20140115', 'cm20140116', 'cm20140122', 'cm20140212', 'cm20140219', 'cm20140226', 'cm20140319', 'cm20140326', 'cm20140409', 'cm20140416', 'cm20140430', 'cm20140507', 'cm20140514', 'cm20140521', 'cm20140522', 'cm20140528', 'cm20140604', 'cm20140611', 'cm20140618', 'cm20140625', 'cm20140702', 'cm20140709']\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import clear_output\n",
      "import sys\n",
      "\n",
      "def crawl_xml(meeting):\n",
      "    # This logic is translated from the official JS code\n",
      "    yy, mm, dd = map(lambda i: int(meeting[i:(i + 2)]), [4, 6, 8])\n",
      "    if mm >= 10:\n",
      "        yr = 'yr%02d-%02d' % (yy, yy + 1)\n",
      "    else:\n",
      "        yr = 'yr%02d-%02d' % (yy - 1, yy)\n",
      "    prefix = 'http://www.legco.gov.hk'\n",
      "    url = '%(prefix)s/%(yr)s/chinese/counmtg/voting/cm_vote_20%(yy)02d%(mm)02d%(dd)02d.xml' % locals()\n",
      "    return requests.get(url)\n",
      "\n",
      "vote_xmls = []\n",
      "for m in meetings:\n",
      "    vote_xmls.append(crawl_xml(m))\n",
      "    clear_output()\n",
      "    print 'progress: %s/%s %s' % (len(vote_xmls), len(meetings), '#' * len(vote_xmls))\n",
      "    sys.stdout.flush()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "progress: 72/72 ########################################################################\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vote_xmls = filter(lambda r: r.ok, vote_xmls)\n",
      "vote_xmls = map(lambda r: r.content, vote_xmls)\n",
      "print len(vote_xmls)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "46\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Information fields, useful for reviewing the result\n",
      "info_fields = ['vote-date', 'vote-time', 'motion-en', 'mover-en', 'mover-type', 'vote-separate-mechanism']\n",
      "def xml_to_records(xml):\n",
      "    doc = etree.XML(xml)\n",
      "    records = []\n",
      "    for topic in doc.xpath('//legcohk-vote/meeting/vote'):\n",
      "        info = [topic.xpath(f)[0].text for f in info_fields]\n",
      "        date = info[0]\n",
      "        topic_id = '%s-%s' % (date, topic.attrib['number'])\n",
      "        for member in topic.xpath('individual-votes/member'):\n",
      "            member_id = member.attrib['name-en'] # Use English name as ID for sipmlicity\n",
      "            vote = member.xpath('vote')[0].text\n",
      "            records.append((topic_id, member_id, vote) + tuple(info))\n",
      "    return records\n",
      "\n",
      "records = reduce(list.__add__, map(xml_to_records, vote_xmls), [])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# More:\n",
      "# http://nbviewer.ipython.org/urls/course.ie.cuhk.edu.hk/~engg4030/tutorial/tutorial7/Legco-Preprocessing.ipynb\n",
      "def clean_record(t):\n",
      "    # According to the numbers, they seem to be the same person\n",
      "    t = list(t)\n",
      "    if t[1] == 'Dr Joseph LEE':\n",
      "        t[1] = 'Prof Joseph LEE'\n",
      "    # Other normalization if any\n",
      "    # ...\n",
      "    return tuple(t)\n",
      "records = map(clean_record, records)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(records, columns = ['topic_id', 'member_id', 'vote'] + info_fields)\n",
      "df.to_csv('records-all-with-info.csv', encoding='utf-8')\n",
      "df[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>topic_id</th>\n",
        "      <th>member_id</th>\n",
        "      <th>vote</th>\n",
        "      <th>vote-date</th>\n",
        "      <th>vote-time</th>\n",
        "      <th>motion-en</th>\n",
        "      <th>mover-en</th>\n",
        "      <th>mover-type</th>\n",
        "      <th>vote-separate-mechanism</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 17/10/2012-1</td>\n",
        "      <td> TSANG Yok-sing</td>\n",
        "      <td> Present</td>\n",
        "      <td> 17/10/2012</td>\n",
        "      <td> 19:37:53</td>\n",
        "      <td> AMENDMENT BY DR HON KENNETH CHAN TO HON IP KIN...</td>\n",
        "      <td> Dr Kenneth CHAN</td>\n",
        "      <td> Member</td>\n",
        "      <td> Yes</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 17/10/2012-1</td>\n",
        "      <td>      Albert HO</td>\n",
        "      <td>     Yes</td>\n",
        "      <td> 17/10/2012</td>\n",
        "      <td> 19:37:53</td>\n",
        "      <td> AMENDMENT BY DR HON KENNETH CHAN TO HON IP KIN...</td>\n",
        "      <td> Dr Kenneth CHAN</td>\n",
        "      <td> Member</td>\n",
        "      <td> Yes</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 17/10/2012-1</td>\n",
        "      <td>  LEE Cheuk-yan</td>\n",
        "      <td>     Yes</td>\n",
        "      <td> 17/10/2012</td>\n",
        "      <td> 19:37:53</td>\n",
        "      <td> AMENDMENT BY DR HON KENNETH CHAN TO HON IP KIN...</td>\n",
        "      <td> Dr Kenneth CHAN</td>\n",
        "      <td> Member</td>\n",
        "      <td> Yes</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 17/10/2012-1</td>\n",
        "      <td>       James TO</td>\n",
        "      <td>     Yes</td>\n",
        "      <td> 17/10/2012</td>\n",
        "      <td> 19:37:53</td>\n",
        "      <td> AMENDMENT BY DR HON KENNETH CHAN TO HON IP KIN...</td>\n",
        "      <td> Dr Kenneth CHAN</td>\n",
        "      <td> Member</td>\n",
        "      <td> Yes</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 17/10/2012-1</td>\n",
        "      <td>   CHAN Kam-lam</td>\n",
        "      <td>      No</td>\n",
        "      <td> 17/10/2012</td>\n",
        "      <td> 19:37:53</td>\n",
        "      <td> AMENDMENT BY DR HON KENNETH CHAN TO HON IP KIN...</td>\n",
        "      <td> Dr Kenneth CHAN</td>\n",
        "      <td> Member</td>\n",
        "      <td> Yes</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "       topic_id       member_id     vote   vote-date vote-time  \\\n",
        "0  17/10/2012-1  TSANG Yok-sing  Present  17/10/2012  19:37:53   \n",
        "1  17/10/2012-1       Albert HO      Yes  17/10/2012  19:37:53   \n",
        "2  17/10/2012-1   LEE Cheuk-yan      Yes  17/10/2012  19:37:53   \n",
        "3  17/10/2012-1        James TO      Yes  17/10/2012  19:37:53   \n",
        "4  17/10/2012-1    CHAN Kam-lam       No  17/10/2012  19:37:53   \n",
        "\n",
        "                                           motion-en         mover-en  \\\n",
        "0  AMENDMENT BY DR HON KENNETH CHAN TO HON IP KIN...  Dr Kenneth CHAN   \n",
        "1  AMENDMENT BY DR HON KENNETH CHAN TO HON IP KIN...  Dr Kenneth CHAN   \n",
        "2  AMENDMENT BY DR HON KENNETH CHAN TO HON IP KIN...  Dr Kenneth CHAN   \n",
        "3  AMENDMENT BY DR HON KENNETH CHAN TO HON IP KIN...  Dr Kenneth CHAN   \n",
        "4  AMENDMENT BY DR HON KENNETH CHAN TO HON IP KIN...  Dr Kenneth CHAN   \n",
        "\n",
        "  mover-type vote-separate-mechanism  \n",
        "0     Member                     Yes  \n",
        "1     Member                     Yes  \n",
        "2     Member                     Yes  \n",
        "3     Member                     Yes  \n",
        "4     Member                     Yes  \n",
        "\n",
        "[5 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = df[['topic_id', 'member_id', 'vote']]\n",
      "df.to_csv('records-all.csv', encoding='utf-8')\n",
      "df[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>topic_id</th>\n",
        "      <th>member_id</th>\n",
        "      <th>vote</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 17/10/2012-1</td>\n",
        "      <td> TSANG Yok-sing</td>\n",
        "      <td> Present</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 17/10/2012-1</td>\n",
        "      <td>      Albert HO</td>\n",
        "      <td>     Yes</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 17/10/2012-1</td>\n",
        "      <td>  LEE Cheuk-yan</td>\n",
        "      <td>     Yes</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 17/10/2012-1</td>\n",
        "      <td>       James TO</td>\n",
        "      <td>     Yes</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 17/10/2012-1</td>\n",
        "      <td>   CHAN Kam-lam</td>\n",
        "      <td>      No</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "       topic_id       member_id     vote\n",
        "0  17/10/2012-1  TSANG Yok-sing  Present\n",
        "1  17/10/2012-1       Albert HO      Yes\n",
        "2  17/10/2012-1   LEE Cheuk-yan      Yes\n",
        "3  17/10/2012-1        James TO      Yes\n",
        "4  17/10/2012-1    CHAN Kam-lam       No\n",
        "\n",
        "[5 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Let's play"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'total # of topics:', len(df['topic_id'].unique())\n",
      "print 'total # of members:',len(df['member_id'].unique())\n",
      "print 'total # of records:', len(df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "total # of topics: 1055\n",
        "total # of members: 70\n",
        "total # of records: 73850\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df['vote'].unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['Present' 'Yes' 'No' 'Absent' 'Abstain']\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df['member_id'].unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['TSANG Yok-sing' 'Albert HO' 'LEE Cheuk-yan' 'James TO' 'CHAN Kam-lam'\n",
        " 'LEUNG Yiu-chung' 'Dr LAU Wong-fat' 'Emily LAU' 'TAM Yiu-chung'\n",
        " 'Abraham SHEK' 'Tommy CHEUNG' 'Frederick FUNG' 'Vincent FANG'\n",
        " 'WONG Kwok-hing' 'Prof Joseph LEE' 'Jeffrey LAM' 'Andrew LEUNG'\n",
        " 'WONG Ting-kwong' 'Ronny TONG' 'Cyd HO' 'Starry LEE' 'Dr LAM Tai-fai'\n",
        " 'CHAN Hak-kan' 'CHAN Kin-por' 'Dr Priscilla LEUNG' 'Dr LEUNG Ka-lau'\n",
        " 'CHEUNG Kwok-che' 'WONG Kwok-kin' 'IP Kwok-him' 'Mrs Regina IP' 'Paul TSE'\n",
        " 'Alan LEONG' 'LEUNG Kwok-hung' 'Albert CHAN' 'WONG Yuk-man' 'Claudia MO'\n",
        " 'Michael TIEN' 'James TIEN' 'NG Leung-sing' 'Steven HO' 'Frankie YICK'\n",
        " 'WU Chi-wai' 'YIU Si-wing' 'Gary FAN' 'MA Fung-kwok' 'Charles Peter MOK'\n",
        " 'CHAN Chi-chuen' 'CHAN Han-pan' 'Dr Kenneth CHAN' 'CHAN Yuen-han'\n",
        " 'LEUNG Che-cheung' 'Kenneth LEUNG' 'Alice MAK' 'Dr KWOK Ka-ki'\n",
        " 'KWOK Wai-keung' 'Dennis KWOK' 'Christopher CHEUNG' 'Dr Fernando CHEUNG'\n",
        " 'SIN Chung-kai' 'Dr Helena WONG' 'IP Kin-yuen' 'Dr Elizabeth QUAT'\n",
        " 'Martin LIAO' 'POON Siu-ping' 'TANG Ka-piu' 'Dr CHIANG Lai-wan'\n",
        " 'Ir Dr LO Wai-kwok' 'CHUNG Kwok-pan' 'Christopher CHUNG' 'Tony TSE']\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df['topic_id'].unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['17/10/2012-1' '17/10/2012-2' '17/10/2012-3' ..., '27/03/2014-13'\n",
        " '27/03/2014-14' '16/04/2014-1']\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# A leader board of voting types\n",
      "board_pos = pd.DataFrame(index=range(0,5))\n",
      "for v in df['vote'].unique():\n",
      "    count = df[df['vote']==v].groupby('member_id').count().sort('vote', ascending=False)['vote']\n",
      "    count = count.reset_index()[:5]\n",
      "    board_pos[v] = pd.Series(zip(count['member_id'], count['vote']), index=range(0,5))\n",
      "board_pos"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Present</th>\n",
        "      <th>Yes</th>\n",
        "      <th>No</th>\n",
        "      <th>Absent</th>\n",
        "      <th>Abstain</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>   (TSANG Yok-sing, 1054)</td>\n",
        "      <td>   (LEUNG Kwok-hung, 685)</td>\n",
        "      <td>        (YIU Si-wing, 856)</td>\n",
        "      <td> (Dr LEUNG Ka-lau, 803)</td>\n",
        "      <td>        (Gary FAN, 238)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>         (Albert HO, 295)</td>\n",
        "      <td>    (CHAN Chi-chuen, 672)</td>\n",
        "      <td>       (Andrew LEUNG, 854)</td>\n",
        "      <td> (LEUNG Yiu-chung, 799)</td>\n",
        "      <td>    (MA Fung-kwok, 113)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>            (Cyd HO, 209)</td>\n",
        "      <td>       (Albert CHAN, 405)</td>\n",
        "      <td>  (Ir Dr LO Wai-kwok, 853)</td>\n",
        "      <td>    (WONG Yuk-man, 788)</td>\n",
        "      <td> (Prof Joseph LEE, 108)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>        (WU Chi-wai, 150)</td>\n",
        "      <td> (Charles Peter MOK, 264)</td>\n",
        "      <td> (Christopher CHEUNG, 842)</td>\n",
        "      <td>  (Frederick FUNG, 776)</td>\n",
        "      <td>     (IP Kwok-him, 105)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> (Charles Peter MOK, 148)</td>\n",
        "      <td>          (Gary FAN, 245)</td>\n",
        "      <td>      (TAM Yiu-chung, 800)</td>\n",
        "      <td>        (James TO, 763)</td>\n",
        "      <td>    (CHAN Kam-lam, 102)</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "                    Present                       Yes  \\\n",
        "0    (TSANG Yok-sing, 1054)    (LEUNG Kwok-hung, 685)   \n",
        "1          (Albert HO, 295)     (CHAN Chi-chuen, 672)   \n",
        "2             (Cyd HO, 209)        (Albert CHAN, 405)   \n",
        "3         (WU Chi-wai, 150)  (Charles Peter MOK, 264)   \n",
        "4  (Charles Peter MOK, 148)           (Gary FAN, 245)   \n",
        "\n",
        "                          No                  Absent                 Abstain  \n",
        "0         (YIU Si-wing, 856)  (Dr LEUNG Ka-lau, 803)         (Gary FAN, 238)  \n",
        "1        (Andrew LEUNG, 854)  (LEUNG Yiu-chung, 799)     (MA Fung-kwok, 113)  \n",
        "2   (Ir Dr LO Wai-kwok, 853)     (WONG Yuk-man, 788)  (Prof Joseph LEE, 108)  \n",
        "3  (Christopher CHEUNG, 842)   (Frederick FUNG, 776)      (IP Kwok-him, 105)  \n",
        "4       (TAM Yiu-chung, 800)         (James TO, 763)     (CHAN Kam-lam, 102)  \n",
        "\n",
        "[5 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "board_neg = pd.DataFrame(index=range(0,5))\n",
      "for v in df['vote'].unique():\n",
      "    count = df[df['vote']==v].groupby('member_id').count().sort('vote', ascending=True)['vote']\n",
      "    count = count.reset_index()[:5]\n",
      "    board_neg[v] = pd.Series(zip(count['member_id'], count['vote']), index=range(0,5))\n",
      "board_neg"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Present</th>\n",
        "      <th>Yes</th>\n",
        "      <th>No</th>\n",
        "      <th>Absent</th>\n",
        "      <th>Abstain</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>     (YIU Si-wing, 1)</td>\n",
        "      <td> (Dr LAU Wong-fat, 23)</td>\n",
        "      <td> (Prof Joseph LEE, 52)</td>\n",
        "      <td>     (TSANG Yok-sing, 1)</td>\n",
        "      <td>  (Frederick FUNG, 11)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> (LEUNG Kwok-hung, 1)</td>\n",
        "      <td>    (Abraham SHEK, 60)</td>\n",
        "      <td>      (Claudia MO, 55)</td>\n",
        "      <td> (Ir Dr LO Wai-kwok, 19)</td>\n",
        "      <td>    (Vincent FANG, 13)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>    (Andrew LEUNG, 1)</td>\n",
        "      <td>  (Dr LAM Tai-fai, 65)</td>\n",
        "      <td>       (Albert HO, 56)</td>\n",
        "      <td>       (YIU Si-wing, 20)</td>\n",
        "      <td>     (Dennis KWOK, 13)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>    (CHAN Kin-por, 1)</td>\n",
        "      <td>    (Vincent FANG, 71)</td>\n",
        "      <td>     (Dennis KWOK, 57)</td>\n",
        "      <td>     (POON Siu-ping, 22)</td>\n",
        "      <td>      (Claudia MO, 13)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>   (LEE Cheuk-yan, 2)</td>\n",
        "      <td>     (Jeffrey LAM, 75)</td>\n",
        "      <td>      (Ronny TONG, 57)</td>\n",
        "      <td>     (TAM Yiu-chung, 30)</td>\n",
        "      <td> (Dr Kenneth CHAN, 14)</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "                Present                    Yes                     No  \\\n",
        "0      (YIU Si-wing, 1)  (Dr LAU Wong-fat, 23)  (Prof Joseph LEE, 52)   \n",
        "1  (LEUNG Kwok-hung, 1)     (Abraham SHEK, 60)       (Claudia MO, 55)   \n",
        "2     (Andrew LEUNG, 1)   (Dr LAM Tai-fai, 65)        (Albert HO, 56)   \n",
        "3     (CHAN Kin-por, 1)     (Vincent FANG, 71)      (Dennis KWOK, 57)   \n",
        "4    (LEE Cheuk-yan, 2)      (Jeffrey LAM, 75)       (Ronny TONG, 57)   \n",
        "\n",
        "                    Absent                Abstain  \n",
        "0      (TSANG Yok-sing, 1)   (Frederick FUNG, 11)  \n",
        "1  (Ir Dr LO Wai-kwok, 19)     (Vincent FANG, 13)  \n",
        "2        (YIU Si-wing, 20)      (Dennis KWOK, 13)  \n",
        "3      (POON Siu-ping, 22)       (Claudia MO, 13)  \n",
        "4      (TAM Yiu-chung, 30)  (Dr Kenneth CHAN, 14)  \n",
        "\n",
        "[5 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_matrix = pd.DataFrame(index=df['member_id'].unique())\n",
      "for gn, g in df.groupby('topic_id'):\n",
      "    df_matrix[gn] = g.set_index('member_id')['vote']\n",
      "df_matrix[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>01/02/2013-1</th>\n",
        "      <th>01/02/2013-2</th>\n",
        "      <th>01/02/2013-3</th>\n",
        "      <th>01/02/2013-4</th>\n",
        "      <th>01/02/2013-5</th>\n",
        "      <th>01/02/2013-6</th>\n",
        "      <th>01/02/2013-7</th>\n",
        "      <th>01/02/2013-8</th>\n",
        "      <th>03/07/2013-1</th>\n",
        "      <th>03/07/2013-10</th>\n",
        "      <th>03/07/2013-2</th>\n",
        "      <th>03/07/2013-3</th>\n",
        "      <th>03/07/2013-4</th>\n",
        "      <th>03/07/2013-5</th>\n",
        "      <th>03/07/2013-6</th>\n",
        "      <th>03/07/2013-7</th>\n",
        "      <th>03/07/2013-8</th>\n",
        "      <th>03/07/2013-9</th>\n",
        "      <th>04/07/2013-11</th>\n",
        "      <th>04/12/2013-1</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>TSANG Yok-sing</th>\n",
        "      <td> Present</td>\n",
        "      <td> Present</td>\n",
        "      <td> Present</td>\n",
        "      <td> Present</td>\n",
        "      <td> Present</td>\n",
        "      <td> Present</td>\n",
        "      <td> Present</td>\n",
        "      <td> Present</td>\n",
        "      <td> Present</td>\n",
        "      <td> Present</td>\n",
        "      <td> Present</td>\n",
        "      <td> Present</td>\n",
        "      <td> Present</td>\n",
        "      <td> Present</td>\n",
        "      <td> Present</td>\n",
        "      <td> Present</td>\n",
        "      <td> Present</td>\n",
        "      <td> Present</td>\n",
        "      <td> Present</td>\n",
        "      <td> Present</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Albert HO</th>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>      No</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>      No</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>      No</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>      No</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LEE Cheuk-yan</th>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>      No</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>      No</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td> Abstain</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>      No</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>James TO</th>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>      No</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>      No</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>      No</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>      No</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CHAN Kam-lam</th>\n",
        "      <td>      No</td>\n",
        "      <td>      No</td>\n",
        "      <td>      No</td>\n",
        "      <td>      No</td>\n",
        "      <td> Abstain</td>\n",
        "      <td>      No</td>\n",
        "      <td>      No</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>      No</td>\n",
        "      <td> Abstain</td>\n",
        "      <td>     Yes</td>\n",
        "      <td> Abstain</td>\n",
        "      <td> Abstain</td>\n",
        "      <td> Abstain</td>\n",
        "      <td> Abstain</td>\n",
        "      <td> Abstain</td>\n",
        "      <td> Abstain</td>\n",
        "      <td> Abstain</td>\n",
        "      <td> Abstain</td>\n",
        "      <td>     Yes</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 1055 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "               01/02/2013-1 01/02/2013-2 01/02/2013-3 01/02/2013-4  \\\n",
        "TSANG Yok-sing      Present      Present      Present      Present   \n",
        "Albert HO               Yes          Yes          Yes          Yes   \n",
        "LEE Cheuk-yan           Yes          Yes          Yes          Yes   \n",
        "James TO                Yes          Yes          Yes          Yes   \n",
        "CHAN Kam-lam             No           No           No           No   \n",
        "\n",
        "               01/02/2013-5 01/02/2013-6 01/02/2013-7 01/02/2013-8  \\\n",
        "TSANG Yok-sing      Present      Present      Present      Present   \n",
        "Albert HO                No          Yes          Yes           No   \n",
        "LEE Cheuk-yan           Yes          Yes          Yes           No   \n",
        "James TO                 No          Yes          Yes           No   \n",
        "CHAN Kam-lam        Abstain           No           No          Yes   \n",
        "\n",
        "               03/07/2013-1 03/07/2013-10 03/07/2013-2 03/07/2013-3  \\\n",
        "TSANG Yok-sing      Present       Present      Present      Present   \n",
        "Albert HO               Yes           Yes           No          Yes   \n",
        "LEE Cheuk-yan           Yes           Yes           No          Yes   \n",
        "James TO                Yes           Yes           No          Yes   \n",
        "CHAN Kam-lam             No       Abstain          Yes      Abstain   \n",
        "\n",
        "               03/07/2013-4 03/07/2013-5 03/07/2013-6 03/07/2013-7  \\\n",
        "TSANG Yok-sing      Present      Present      Present      Present   \n",
        "Albert HO               Yes          Yes          Yes          Yes   \n",
        "LEE Cheuk-yan           Yes          Yes          Yes          Yes   \n",
        "James TO                Yes          Yes          Yes          Yes   \n",
        "CHAN Kam-lam        Abstain      Abstain      Abstain      Abstain   \n",
        "\n",
        "               03/07/2013-8 03/07/2013-9 04/07/2013-11 04/12/2013-1      \n",
        "TSANG Yok-sing      Present      Present       Present      Present ...  \n",
        "Albert HO               Yes          Yes           Yes           No ...  \n",
        "LEE Cheuk-yan       Abstain          Yes           Yes           No ...  \n",
        "James TO                Yes          Yes           Yes           No ...  \n",
        "CHAN Kam-lam        Abstain      Abstain       Abstain          Yes ...  \n",
        "\n",
        "[5 rows x 1055 columns]"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def to_numeric(x):\n",
      "    x[(x != 'Yes') & (x != 'No')] = 0\n",
      "    x[x == 'Yes'] = 1\n",
      "    x[x == 'No'] = -1\n",
      "df_matrix.apply(to_numeric)\n",
      "df_matrix[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>01/02/2013-1</th>\n",
        "      <th>01/02/2013-2</th>\n",
        "      <th>01/02/2013-3</th>\n",
        "      <th>01/02/2013-4</th>\n",
        "      <th>01/02/2013-5</th>\n",
        "      <th>01/02/2013-6</th>\n",
        "      <th>01/02/2013-7</th>\n",
        "      <th>01/02/2013-8</th>\n",
        "      <th>03/07/2013-1</th>\n",
        "      <th>03/07/2013-10</th>\n",
        "      <th>03/07/2013-2</th>\n",
        "      <th>03/07/2013-3</th>\n",
        "      <th>03/07/2013-4</th>\n",
        "      <th>03/07/2013-5</th>\n",
        "      <th>03/07/2013-6</th>\n",
        "      <th>03/07/2013-7</th>\n",
        "      <th>03/07/2013-8</th>\n",
        "      <th>03/07/2013-9</th>\n",
        "      <th>04/07/2013-11</th>\n",
        "      <th>04/12/2013-1</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>TSANG Yok-sing</th>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Albert HO</th>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td> -1</td>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td> -1</td>\n",
        "      <td>  1</td>\n",
        "      <td> 1</td>\n",
        "      <td> -1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> -1</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LEE Cheuk-yan</th>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td> -1</td>\n",
        "      <td>  1</td>\n",
        "      <td> 1</td>\n",
        "      <td> -1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> -1</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>James TO</th>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td> -1</td>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td> -1</td>\n",
        "      <td>  1</td>\n",
        "      <td> 1</td>\n",
        "      <td> -1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> -1</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CHAN Kam-lam</th>\n",
        "      <td> -1</td>\n",
        "      <td> -1</td>\n",
        "      <td> -1</td>\n",
        "      <td> -1</td>\n",
        "      <td>  0</td>\n",
        "      <td> -1</td>\n",
        "      <td> -1</td>\n",
        "      <td>  1</td>\n",
        "      <td> -1</td>\n",
        "      <td> 0</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  1</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 1055 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "               01/02/2013-1 01/02/2013-2 01/02/2013-3 01/02/2013-4  \\\n",
        "TSANG Yok-sing            0            0            0            0   \n",
        "Albert HO                 1            1            1            1   \n",
        "LEE Cheuk-yan             1            1            1            1   \n",
        "James TO                  1            1            1            1   \n",
        "CHAN Kam-lam             -1           -1           -1           -1   \n",
        "\n",
        "               01/02/2013-5 01/02/2013-6 01/02/2013-7 01/02/2013-8  \\\n",
        "TSANG Yok-sing            0            0            0            0   \n",
        "Albert HO                -1            1            1           -1   \n",
        "LEE Cheuk-yan             1            1            1           -1   \n",
        "James TO                 -1            1            1           -1   \n",
        "CHAN Kam-lam              0           -1           -1            1   \n",
        "\n",
        "               03/07/2013-1 03/07/2013-10 03/07/2013-2 03/07/2013-3  \\\n",
        "TSANG Yok-sing            0             0            0            0   \n",
        "Albert HO                 1             1           -1            1   \n",
        "LEE Cheuk-yan             1             1           -1            1   \n",
        "James TO                  1             1           -1            1   \n",
        "CHAN Kam-lam             -1             0            1            0   \n",
        "\n",
        "               03/07/2013-4 03/07/2013-5 03/07/2013-6 03/07/2013-7  \\\n",
        "TSANG Yok-sing            0            0            0            0   \n",
        "Albert HO                 1            1            1            1   \n",
        "LEE Cheuk-yan             1            1            1            1   \n",
        "James TO                  1            1            1            1   \n",
        "CHAN Kam-lam              0            0            0            0   \n",
        "\n",
        "               03/07/2013-8 03/07/2013-9 04/07/2013-11 04/12/2013-1      \n",
        "TSANG Yok-sing            0            0             0            0 ...  \n",
        "Albert HO                 1            1             1           -1 ...  \n",
        "LEE Cheuk-yan             0            1             1           -1 ...  \n",
        "James TO                  1            1             1           -1 ...  \n",
        "CHAN Kam-lam              0            0             0            1 ...  \n",
        "\n",
        "[5 rows x 1055 columns]"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = matrix(df_matrix.as_matrix()).astype('float')\n",
      "X = X - mean(X, 0)\n",
      "C = X.T * X\n",
      "print C.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(1055, 1055)\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy.sparse.linalg\n",
      "PA = scipy.sparse.linalg.eigs(C, k=2, which='LM', return_eigenvectors=True)[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Use the following one to pop up the 3D plot in another window.\n",
      "# Good for interactive exploration.\n",
      "# May or may not be available due to your desktop.\n",
      "#%pylab \n",
      "#matplotlib.use('TkAgg') \n",
      "# Use the following one to plot inline (embedded in this notebook).\n",
      "#%pylab inline\n",
      "\n",
      "\n",
      "# Try 3D\n",
      "PA = scipy.sparse.linalg.eigs(C, k=3, which='LM', return_eigenvectors=True)[1]\n",
      "# Project data points onto principle axis\n",
      "X_3D = PA.T * X.T\n",
      "print X_3D.shape\n",
      "# We intentionally add some disturbance for better visualization.\n",
      "# Or else, some of the nodes are located in the same place.\n",
      "# (Those who vote exactly the same)\n",
      "X_3D = X_3D + randn(*tuple(X_3D.shape)) * 0.3\n",
      "x = array(X_3D[0, :]).astype('float')\n",
      "y = array(X_3D[1, :]).astype('float')\n",
      "z = array(X_3D[2, :]).astype('float')\n",
      "\n",
      "from mpl_toolkits.mplot3d import Axes3D\n",
      "fig = figure()\n",
      "ax = fig.add_subplot(111, projection='3d')\n",
      "ax.scatter(x, y, z, picker=True, s=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(3, 70)\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:19: ComplexWarning: Casting complex values to real discards the imaginary part\n",
        "-c:20: ComplexWarning: Casting complex values to real discards the imaginary part\n",
        "-c:21: ComplexWarning: Casting complex values to real discards the imaginary part\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<mpl_toolkits.mplot3d.art3d.Patch3DCollection at 0x107bd0850>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCIV0FShQoEBG/H/cQouCFKGvwwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107ab5110>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_pc1 = pd.DataFrame(x.flatten(), index=df_matrix.index, columns=['PC1'])\n",
      "df_pc1 = df_pc1.sort('PC1')\n",
      "figure(figsize(12, 20))\n",
      "plot(df_pc1['PC1'], df_pc1.rank()['PC1'], 'd', markersize=10)\n",
      "#yticks(df_pc1.rank()['PC1'], df_pc1.index)\n",
      "for (_x, _y, _s) in zip(df_pc1['PC1'], df_pc1.rank()['PC1'], df_pc1.index):\n",
      "    annotate(_s, (_x, _y), xytext=(_x + 0.5, _y - 0.2))\n",
      "title('Spectrum from Principal Component 1')\n",
      "axis([-32, 28, 0, 71])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "[-32, 28, 0, 71]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAArsAAASHCAYAAADiC7VuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107be5bd0>"
       ]
      }
     ],
     "prompt_number": 20
    }
   ],
   "metadata": {}
  }
 ]
}